Reduce Latency in Azure Stream Analytics Queries
Question
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency.
You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answers
Explanations
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A. B. C. D. E.CE
C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming Analytics job) can consume and write different partitions in parallel, which increases throughput.
E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your
Stream Analytics job in a timely manner.
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumptionTo reduce the latency in an Azure Stream Analytics job that returns a result set with 10,000 distinct values for a column named clusterID, you can take the following two actions:
C. Scale out the query by using PARTITION BY: By scaling out the query, you can increase the processing power of the job and distribute the workload across multiple partitions. In the case of the query with 10,000 distinct values, you can use the PARTITION BY clause to partition the data by clusterID. This will distribute the load of processing the distinct values across multiple partitions, reducing the workload on each partition and improving the overall performance of the job.
E. Increase the number of streaming units: Streaming units are the processing resources allocated to a Stream Analytics job. By increasing the number of streaming units, you can increase the processing power of the job and reduce the latency. This is especially useful when dealing with large datasets or complex queries that require more resources to process.
A. Adding a pass-through query or D. Converting the query to a reference query may not be useful in reducing the latency of the Stream Analytics job. A pass-through query is used when you want to pass the input data directly to the output without any processing, and a reference query is used to join reference data to the input data. However, in the case of the query with 10,000 distinct values, these options may not improve the performance of the job.
B. Adding a temporal analytic function may be useful in processing time-based data or calculating rolling averages, but it may not be useful in reducing the latency of the job in the case of the query with 10,000 distinct values for the clusterID column.
